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Statistics LibreTexts

2.13: Categorical vs. Quantitative Data

  • Page ID
    14026
  • Learning Objectives

    • Distinguish between quantitative and categorical variables in context.

    Data consist of individuals and variables that give us information about those individuals. An individual can be an object or a person. A variable is an attribute, such as a measurement or a label.

    Example

    Medical Records

    This dataset is from a medical study. In this study, researchers wanted to identify variables connected to low birth weights.

    Age at delivery Weight prior to pregnancy (pounds) Smoker Doctor visits during 1st trimester Race Birth Weight (grams)
    Patient 1 29 140 Yes 2 Caucasian 2977
    Patient 2 32 132 No 4 Caucasian 3080
    Patient 3 36 175 No 0 African-American 3600
    * * * * * * *
    * * * * * * *
    Patient 189 30 95 Yes 2 Asian 3147

    In this example, the individuals are the patients (the mothers). There are six variables in this dataset:

    • Mother’s age at delivery (years)
    • Mother’s weight prior to pregnancy (pounds)
    • Whether mother smoked during pregnancy (yes, no)
    • Number of doctor visits during first trimester of pregnancy
    • Mother’s race (Caucasian, African American, Asian, etc.)
    • Baby’s birth weight (grams)

    There are two types of variables: quantitative and categorical.

    • Categorical variables take category or label values and place an individual into one of several groups. Each observation can be placed in only one category, and the categories are mutually exclusive. In our example of medical records, smoking is a categorical variable, with two groups, since each participant can be categorized only as either a nonsmoker or a smoker. Gender and race are the two other categorical variables in our medical records example.
    • Quantitative variables take numerical values and represent some kind of measurement. In our medical example, age is an example of a quantitative variable because it can take on multiple numerical values. It also makes sense to think about it in numerical form; that is, a person can be 18 years old or 80 years old. Weight and height are also examples of quantitative variables.

    Try It

    We took a random sample from the 2000 US Census. Here is part of the dataset.

    Sample of 2000 US Census Data
    State Zipcode Family_Size Annual_Income
    Florida 32716 8 200
    Alabama 35236 5 800
    Florida 32116 6 13500
    Florida 33679 5 21000
    Alabama 36374 4 21000
    California 94565 1 23000

    https://assessments.lumenlearning.co...sessments/3411

    https://assessments.lumenlearning.co...sessments/3412

    Try It

    Consumer Reports analyzed a dataset of 77 breakfast cereals. Here is a part of the dataset.

    (Note: Consumer Reports is an non-profit organization that rates products in an effort to help consumers make informed decisions.)

    Sample of Consumer Reports Breakfast Cereal Data
    Name Manufactuer Target Shelf Calories Sodium Fat
    100% Bran Nabisco adult top 70 130 1
    100% Natural Bran Quaker Oats adult top 120 15 5
    All-Bran Kelloggs adult top 70 260 1
    All-Bran Extra Fiber Kelloggs adult top 50 140 0
    Almond Delight Ralston Purnia adult top 110 200 2
    Apple Cinnamon Cheerios General Mills child bottom 110 180 2
    Apple Jacks Kelloggs child middle 110 125 0

    https://assessments.lumenlearning.co...sessments/3415

    https://assessments.lumenlearning.co...sessments/3416

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